Active Learning for Patch-Based Digital Pathology using Convolutional Neural Networks to Reduce Annotation Costs

Jacob Carse (Lead / Corresponding author), Stephen McKenna

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)
424 Downloads (Pure)


Methods to reduce the need for costly data annotations become increasingly important as deep learning gains popularity in medical image analysis and digital pathology. Active learning is an appealing approach that can reduce the amount of annotated data needed to train machine learning models but traditional active learning strategies do not always work well with deep learning. In patch-based machine learning systems, active learning methods typically request annotations for small individual patches which can be tedious and costly for the annotator who needs to rely on visual context for the patches. We propose an active learning framework that selects regions for annotation that are built up of several patches, which should increase annotation throughput. The framework was evaluated with several query strategies on the task of nuclei classification. Convolutional neural networks were trained on small patches, each containing a single nucleus. Traditional query strategies performed worse than random sampling. A K-centre sampling strategy showed a modest gain. Further investigation is needed in order to achieve significant performance gains using deep active learning for this task.
Original languageEnglish
Title of host publicationDigital Pathology
Subtitle of host publication15th European Congress, ECDP 2019, Warwick, UK, April 10–13, 2019, Proceedings
EditorsConstantino Carlos Reyes-Aldasoro, Andrew Janowczyk, Mitko Veta, Peter Bankhead, Korsuk Sirinukunwattana
Place of PublicationSwitzerland
Number of pages8
ISBN (Electronic)9783030239374
ISBN (Print)9783030239374, 9783030239367
Publication statusPublished - 2019
Event15th European Congress on Digital Pathology (ECDP) - University of Warwick, Warwick, United Kingdom
Duration: 10 Apr 201913 Apr 2019
Conference number: 15th

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11435 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th European Congress on Digital Pathology (ECDP)
Abbreviated titleECDP 2019
Country/TerritoryUnited Kingdom
Internet address


  • Active learning
  • Deep learning
  • Image annotation
  • Nuclei classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Active Learning for Patch-Based Digital Pathology using Convolutional Neural Networks to Reduce Annotation Costs'. Together they form a unique fingerprint.

Cite this